6110 papers
This paper addresses the lack of granular data for skin tone fairness by introducing the large-scale, open-access STW dataset labeled with the 10-tone MST scale, benchmarking deep learning against classic methods, and proposing the SkinToneNet model to achieve state-of-the-art generalization for reliable fairness auditing.
The paper proposes E2E-GNet, an end-to-end geometric deep neural network that utilizes a geometric transformation layer and a distortion-aware optimization layer to effectively project skeleton motion sequences from non-Euclidean to linear space, thereby achieving superior human motion recognition performance with lower computational cost across multiple datasets.
ModalPatch is a plug-and-play module that enhances the robustness of multi-modal 3D object detection under arbitrary modality-drop scenarios by leveraging temporal history to predict missing features and employing an uncertainty-guided fusion strategy to ensure reliable compensation without requiring architectural changes or retraining.
MUSE is an open-source, run-centric platform that addresses the gap in multimodal safety evaluation by integrating automatic cross-modal payload generation, multi-turn attack algorithms with inter-turn modality switching, and a dual-metric framework to demonstrate that alignment often fails to generalize across audio, image, and video inputs, revealing significantly higher attack success rates than single-turn text-based evaluations suggest.
This paper introduces two new geometric structures on the symmetric positive-definite matrix cone derived from James' bicone reparameterization, which ensure geodesics correspond to straight lines, generalize the Hilbert simplex distance, and offer new tools for analyzing dissimilarities across various scientific disciplines.
This paper introduces WTHaar-Net, a hybrid quantum-classical convolutional neural network that replaces the Hadamard Transform with the spatially localized Haar Wavelet Transform to achieve significant parameter reduction and competitive accuracy on image classification tasks while demonstrating compatibility with near-term quantum hardware.
This paper introduces a scalable, markerless 3D human reconstruction framework that extracts biomechanically meaningful markers from video to accurately estimate gait parameters, demonstrating strong agreement with reference marker-based data and outperforming conventional pose-estimation methods for clinical and real-world applications.
This paper proposes the Semantic-Guided Modality-Aware (SGMA) framework, a novel approach for incomplete multimodal semantic segmentation in remote sensing that utilizes Semantic-Guided Fusion and Modality-Aware Sampling modules to effectively address multimodal imbalance, intra-class variation, and cross-modal heterogeneity, thereby outperforming state-of-the-art methods.
This paper demonstrates that replacing rigid anatomical parcellations with functionally-derived regions of interest within a Graph Attention Network ensemble significantly enhances explainable Autism Spectrum Disorder classification accuracy on rs-fMRI data, achieving state-of-the-art performance while identifying biologically relevant Default Mode Network hubs.
NeighborMAE is a self-supervised learning framework that enhances Earth Observation image representation by leveraging the spatial dependencies between neighboring images through joint reconstruction and a dynamic heuristic strategy for mask ratios and loss weighting, resulting in superior performance across various downstream tasks compared to existing baselines.
EIMC is an efficient instance-aware multi-modal collaborative perception framework that adopts an early collaborative paradigm and a heatmap-driven consensus protocol to selectively transmit only critical instance vectors, thereby significantly reducing bandwidth usage while enhancing detection accuracy for occluded objects in autonomous driving.
This paper provides a systematic information-theoretic analysis of the focal-entropy, establishing its mathematical properties and demonstrating how the focal-loss fundamentally alters probability distributions by amplifying mid-range probabilities while suppressing both high-probability and extremely low-probability outcomes in class-imbalanced learning.
This paper introduces ForestPersons, a large-scale dataset of 96,482 under-canopy images with over 200,000 annotations designed to address the limitations of aerial UAV imagery in detecting missing persons during forest Search and Rescue missions.
This paper proposes the Generation-Assisted Discriminative (GAD) classifier, a fine-tuning strategy that leverages the efficiency of discriminative classification while utilizing generative modeling to enhance performance, achieving state-of-the-art accuracy and significantly faster inference for closed-set action understanding in Multimodal Large Language Models.
SemGS is a feed-forward framework that reconstructs generalizable semantic 3D fields from sparse views using a dual-branch architecture with shared CNN layers and camera-aware attention, enabling rapid, state-of-the-art semantic scene understanding and novel view synthesis without scene-specific optimization.
This paper presents a collision-free dual-arm surgical assistive robot that leverages a vision-language model for zero-shot instruction interpretation and a real-time quadratic programming framework to ensure safe, reactive obstacle avoidance while autonomously delivering instruments with an 83.33% success rate.
This paper proposes Generalizable Knowledge Distillation (GKD), a multi-stage framework that decouples representation learning from task adaptation and employs a query-based soft distillation mechanism to effectively transfer robust, domain-agnostic knowledge from vision foundation models to semantic segmentation tasks, significantly improving out-of-domain generalization compared to conventional methods.
This paper proposes VC-STaR, a novel self-improving framework that leverages visual contrastive pairs to mitigate hallucinations in model-generated rationales, resulting in the VisCoR-55K dataset that significantly enhances the visual reasoning capabilities of Vision Language Models.
This paper proposes CAPT, a Confusion-Aware Prompt Tuning framework that mitigates vision-language misalignment by explicitly modeling persistent category confusion through a Confusion Bank and integrating semantic and sample-level cues via specialized miners and a multi-granularity expert to significantly reduce classification errors.
The paper proposes CAWM-Mamba, a unified end-to-end framework that jointly performs infrared-visible image fusion and compound adverse weather restoration using a Weather-Aware Preprocess Module, Cross-modal Feature Interaction Module, and Wavelet Space State Block to outperform existing methods in handling multiple simultaneous degradations while enhancing downstream perception tasks.